Identification of a 16-MTGs Prognostic Signature in Diffuse Large B-Cell Lymphoma

Author:

Wang Shijun1,Wang Xiaoqin2ORCID,Li Guixia3,Feng Pengcheng3ORCID

Affiliation:

1. Department of Chemistry, Changzhi University, Changzhi, China

2. Department of Anesthesiology, Changzhi Medical College, Changzhi, China

3. Department of Basic Medicine, Changzhi Medical College, Changzhi, China

Abstract

Background. Diffuse large B-cell lymphoma (DLBCL) is one of the largest lymphoma subcategories. Usually, 50%–70% of DLBCL patients can be cured by the standard treatment. But, at least one third have bad prognosis. Based on this situation, the research on DLBCL therapy strategy is still indispensable. Methods. A prognostic signature was built according to the public data and bioinformatics methods, the stability and reliability was assessed and validated. GSEA was performed to explore the difference in different groups. Consensus clustering and immune infiltration analysis were conducted comprehensively. Results. In this work, a signature based on multiple metabolism-associated genes (MTGs) was established, containing 16 MTGs, to predict the prognosis of DLBCL patients. The accuracy and effectiveness of this signature have been verified by three external validation sets. According to the risk formula, DLBCL patients were divided into high- and low-risk groups, and the survival rate of the low-risk group was significantly higher than that of the high-risk group. Furthermore, gene set enrichment analysis (GSEA) demonstrated that beta-alanine metabolism and regulation of actin cytoskeleton signal pathways were enriched in the low-risk group. The actual survival and nomogram-predicted survival matched well both in the training cohort and verification cohorts. Conclusion. In general, our prognostic signature can provide reliable and valuable information for medical workers in predicting the prognosis of DLBCL. A preprint was made available by the research square in the following link: “https://www.researchsquare.com/article/rs-1468741/v2.”

Funder

Applied Basic Research Project of Shanxi Province, China

Publisher

Hindawi Limited

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